String Algorithms and Pattern Matching Questions

Covers algorithmic techniques and practical skills for solving string problems and pattern matching tasks. Core algorithm knowledge includes substring search and pattern matching algorithms such as Knuth Morris Pratt, Rabin Karp, Boyer Moore, Z algorithm, Aho Corasick for multiple pattern matching, and rolling hash methods. Data structures and suffix structures are important, including tries, suffix arrays, suffix trees, and suffix automata, together with longest common prefix arrays and related construction techniques. Also includes dynamic programming approaches for string problems such as edit distance and longest common subsequence, palindrome and anagram detection methods, and regular expression concepts and engine behavior. Emphasizes algorithmic complexity analysis, time and space trade offs, memory and streaming constraints, and optimization strategies for very long inputs and high throughput text processing. Practical considerations include parsing and string manipulation idioms in common languages, Unicode and character encoding issues, edge case handling, test case design for strings, and real world applications such as log analysis, text search, and data transformation.

MediumTechnical

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Implement the Aho-Corasick automaton for multiple-pattern matching. Provide an API with build(patterns: List[str]) and search(text: str) -> List[(pattern_id, index)]. Explain how failure links and output links work, and give complexity in terms of total pattern length P and text length N.

HardTechnical

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Rabin-Karp's rolling hash can be attacked by adversarial inputs crafted to produce collisions. Describe how an attacker can degrade performance and propose mitigations such as randomized base/modulus selection, double hashing, fallback deterministic checks, or cryptographic hashing. Discuss trade-offs between robustness and performance.

MediumTechnical

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Implement Levenshtein edit distance in Java using only O(min(n, m)) space by keeping two rows of the DP table (rolling rows). Signature: int editDistance(String a, String b). Explain when this optimization is applicable and how you would reconstruct the actual edit sequence if required.

HardSystem Design

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Design an incremental autocomplete (typeahead) service for a global user base: low latency per keystroke, millions of terms, frequent updates, and ranking by popularity. Compare approaches: trie with cached top-k per node, suffix array, and inverted index. Discuss update pipelines, memory budgets, sharding, and ranking/pagination strategies.

EasyTechnical

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Explain greedy vs lazy quantifiers in regular expressions and give a concrete example where greedy matching produces a different result from lazy matching. Define catastrophic backtracking, show a regex example that can trigger it, and describe how to mitigate the issue.

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